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Record W2047435067 · doi:10.1155/2015/868519

An Unusual Presentation of a Massive Pulmonary Embolism with Misleading Investigation Results Treated with Tenecteplase

2015· article· en· W2047435067 on OpenAlexaff
David Migneault, Zachary A. Levine, François de Champlain

Bibliographic record

VenueCase Reports in Emergency Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcGill University Health CentreMontreal General HospitalVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsTenecteplaseMedicinePulmonary embolismPresentation (obstetrics)Context (archaeology)Emergency departmentRadiologyIntensive care medicineSurgeryCardiologyThrombolysisMyocardial infarction

Abstract

fetched live from OpenAlex

Background. There is no foolproof strategy to identify a pulmonary embolism (PE) in the emergency department, and atypical presentations are common. Negative test results may mislead physicians away from the diagnosis of PE. Objectives. The current report aims to raise awareness of an unusual presentation of massive PE and its diagnosis and management, in the face of limited evidence in the scientific literature. Case Reports. We report the case of a patient with a negative D-Dimer and a negative Computed Tomography contrast angiography of the chest who was diagnosed twenty-seven hours later with a massive PE, as suggested by a bedside echocardiography. The patient was successfully treated with tenecteplase (TNK). Conclusions/Summary. Pulmonary embolism frequently presents atypically and is often a diagnostic challenge. There is limited literature about the treatment of massive PE. Further research on bedside echocardiography for diagnosing PE in unstable patients is warranted. In addition, further study into new thrombolytic agents like tenecteplase in the context of massive and submassive PE is warranted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.325
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2015
Admission routes1
Has abstractyes

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